论文标题
eqvio：用于视觉惯性探望的模棱两可的过滤器
EqVIO: An Equivariant Filter for Visual Inertial Odometry
论文作者
论文摘要
视觉惯性进程（VIO）是通过将惯性测量单元（IMU）和相机的信息组合到估算机器人轨迹的问题，并且对机器人群社区非常感兴趣。本文为VIO问题开发了一种新颖的谎言组对称性，并应用了最近提出的epoiriant滤波器。对称性显示与VIO参考框架的不变性兼容，导致无偏见的IMU动力学的精确线性化，并提供视觉测量函数的等效性。结果，基于此谎言组的eprovariant滤波器（EQF）是对VIO的一致估计器，在状态动力学传播中，线性化误差较低，而高阶均值输出近似值比标准配方更高。关于流行的Euroc和UZH FPV数据集的实验结果表明，该系统在速度和准确性方面都优于其他最先进的VIO算法。
Visual Inertial Odometry (VIO) is the problem of estimating a robot's trajectory by combining information from an inertial measurement unit (IMU) and a camera, and is of great interest to the robotics community. This paper develops a novel Lie group symmetry for the VIO problem and applies the recently proposed equivariant filter. The symmetry is shown to be compatible with the invariance of the VIO reference frame, lead to exact linearisation of bias-free IMU dynamics, and provide equivariance of the visual measurement function. As a result, the equivariant filter (EqF) based on this Lie group is a consistent estimator for VIO with lower linearisation error in the propagation of state dynamics and a higher order equivariant output approximation than standard formulations. Experimental results on the popular EuRoC and UZH FPV datasets demonstrate that the proposed system outperforms other state-of-the-art VIO algorithms in terms of both speed and accuracy.
